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Big Data Analytics with Hadoop 3

By : Sridhar Alla

3 (1)

Buy this Book

Big Data Analytics with Hadoop 3

3 (1)

By: Sridhar Alla

Buy this Book

Overview of this book

Apache Hadoop is the most popular platform for big data processing, and can be combined with a host of other big data tools to build powerful analytics solutions. Big Data Analytics with Hadoop 3 shows you how to do just that, by providing insights into the software as well as its benefits with the help of practical examples. Once you have taken a tour of Hadoop 3’s latest features, you will get an overview of HDFS, MapReduce, and YARN, and how they enable faster, more efficient big data processing. You will then move on to learning how to integrate Hadoop with the open source tools, such as Python and R, to analyze and visualize data and perform statistical computing on big data. As you get acquainted with all this, you will explore how to use Hadoop 3 with Apache Spark and Apache Flink for real-time data analytics and stream processing. In addition to this, you will understand how to use Hadoop to build analytics solutions on the cloud and an end-to-end pipeline to perform big data analysis using practical use cases. By the end of this book, you will be well-versed with the analytical capabilities of the Hadoop ecosystem. You will be able to build powerful solutions to perform big data analytics and get insight effortlessly.

Preface

Who this book is for

What this book covers

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Free Chapter

Introduction to Hadoop

Hadoop Distributed File System

MapReduce framework

YARN

Other changes

Installing Hadoop 3

Summary

Overview of Big Data Analytics

Introduction to data analytics

Introduction to big data

Distributed computing using Apache Hadoop

The MapReduce framework

Hive

Apache Spark

Visualization using Tableau

Summary

Big Data Processing with MapReduce

The MapReduce framework

MapReduce job types

MapReduce patterns

Summary

Scientific Computing and Big Data Analysis with Python and Hadoop

Installation

Data analysis

Summary

Statistical Big Data Computing with R and Hadoop

Introduction

Methods of integrating R and Hadoop

Data analytics

Summary

Batch Analytics with Apache Spark

SparkSQL and DataFrames

DataFrame APIs and the SQL API

Schema – structure of data

Loading datasets

Saving datasets

Aggregations

Joins

Summary

Real-Time Analytics with Apache Spark

Streaming

Spark Streaming

fileStream

Transformations

Checkpointing

Driver failure recovery

Interoperability with streaming platforms (Apache Kafka)

Handling event time and late date

Fault-tolerance semantics

Summary

Batch Analytics with Apache Flink

Introduction to Apache Flink

Installing Flink

Using the Flink cluster UI

Batch analytics

Summary

Stream Processing with Apache Flink

Introduction to streaming execution model

Data processing using the DataStream API

Summary

Visualizing Big Data

Introduction

Tableau

Chart types

Using Python to visualize data

Using R to visualize data

Big data visualization tools

Summary

Introduction to Cloud Computing

Concepts and terminology

Goals and benefits

Risks and challenges

Roles and boundaries

Cloud characteristics

Cloud delivery models

Cloud deployment models

Summary

Using Amazon Web Services

Amazon Elastic Compute Cloud

Launching multiple instances of an AMI

What is AWS Lambda?

Introduction to Amazon S3

Amazon DynamoDB

Amazon Kinesis Data Streams

AWS Glue

Amazon EMR

Summary

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Batch Analytics with Apache Spark

In this chapter, you will learn about Apache Spark and how to use it for big data analytics based on a batch processing model. Spark SQL is a component on top of Spark Core that can be used to query structured data. It is becoming the de facto tool, replacing Hive as the choice for batch analytics on Hadoop.

Moreover, you will learn how to use Spark for the analysis of structured data (unstructured data such as a document containing arbitrary text, or some other format that has to be transformed into a structured form). We will see how DataFrames/datasets are the cornerstone here, and how SparkSQL's APIs make querying structured data simple yet robust.

We will also introduce datasets and see the difference between datasets, DataFrames, and RDDs. In a nutshell, the following topics will be covered in this chapter:

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